We present a novel method for detecting the boundaries between objects in images that uses a large, hierarchical, semantic ontology -- WordNet. The semantic object hierarchy in WordNet grounds this ill-posed segmentation problem, so that true boundaries are defined as edges between instances of different classes, and all other edges are clutter. To avoid fully classifying each pixel, which is very difficult in generic images, we evaluate the semantic similarity of the two regions bounding each edge in an initial oversegmentation. Semantic similarity is computed using WordNet enhanced with appearance information, and is largely orthogonal to visual similarity. Hence two regions with very similar visual attributes, but from different categories, can have a large semantic distance and therefore evidence of a strong boundary between them, and vice versa. The ontology is trained with images from the UC Berkeley image segmentation benchmark, extended with manual labeling of the semantic content of each image segment. Results on boundary detection against the benchmark images show that semantic similarity computed through WordNet can significantly improve boundary detection compared to generic segmentation.

Subjects: 19. Vision; 12. Machine Learning and Discovery

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